Fingerprint verification system

ABSTRACT

A biometric verification system for controlling access is provided that does not rely on a non-biometric discriminator, such as a PIN or magnetic card, to convert a one-to-many verification task to a one-to-one verification task. The system enrolls authorized users by obtaining digitized fingerprint templates from them and storing them in a database. Video cameras and fingerprint sensors are provided for use in authenticating persons seeking access. Software compares a digital representation of a captured human facial image with stored facial images in a database of facial images, generating a match confidence therefrom and rank-ordering the database from highest to lowest match confidence. The software then compares captured human fingerprints with stored fingerprint templates associated with the rank-ordered database to verify the identity of the person and provide an output signal indicative of recognition.

RELATED APPLICATIONS

This Application is a continuation of Applicant's copending U.S. patentapplication Ser. No. 09/952,096, filed Sep. 12, 2001, which is herebyincorporated by reference, and which in turn claims priority to U.S.Provisional Patent Application No. 60/232,924, filed Sep. 15, 2000. ThisApplication claims domestic priority, under 35 U.S.C. § 119(e)(1), tothe earliest filing date of Sep. 15, 2000.

FIELD OF THE INVENTION

The present invention is generally directed to a method and apparatusfor recognizing human users and more particularly providing biometricsecurity by identifying and verifying a fingerprint of an authorizedhuman user and producing an output signal indicative of recognition ornon-recognition of said human user. The present invention also relatesto providing rapid identification of an individual's fingerprint in alarge database of fingerprints through the use of a facial imagerecognition pre-processing and search ordering method. The presentinvention further relates to layering multiple biometric techniques forproviding increased levels of security.

BACKGROUND OF THE INVENTION

In light of the myriad technological advancements that havecharacterized the previous decade, providing high security for computersystems and facilities has become a daunting challenge. Even as recentstatistics are showing a decline in the overall violent crime rate,theft and more particularly technology related crime, has soared. Theproblem is costing insurance companies, and U.S. citizens, billions ofdollars each year. Hackers who have successfully introduced computerviruses through email and other means have cost corporations millions ifnot billions of dollars in repair costs, lost work product and lostrevenue. Because of this sophisticated criminal environment, manycompanies, government agencies and individuals alike have begun to viewbiometric security applications in a far more favorable light, however,biometric identification techniques (recognizing an individual based ona physiological metric), have yet to be employed either due to theircomplexity, invasiveness (lengthy recognition delays) or high cost.

There exists many methods for providing security against fraud and theftincluding conventional keys, remote keyless entry systems, key padinterfaces which require the user to enter a Personal IdentificationNumber (PIN), alarm systems, magnetic card systems and proximity devicesystems. Similarly there exists many methods for the biometricidentification of humans which includes facial image verification, voicerecognition, iris scanning, retina imaging as well as fingerprintpattern matching.

Biometric verification systems work best when employed in a one-to-oneverification mode (comparing one unknown biometric to one knownbiometric). When biometric verification is used in a one-to-many mode(comparing one unknown biometric to a database of known biometrics) suchas one might employ in a facility security application, processingdelays caused by the inefficiency of searching the entire database for amatch are often unacceptable when the number of users exceeds 20 to 30individuals. This makes most biometric applications unsuitable forlarger user databases. In order to circumvent this limitation, abiometric verification algorithm is typically integrated with anon-biometric device such as a PIN keypad. The advantage to thisarrangement is that a one-to-many verification scenario can be reducedto a one-to-one verification scenario by limiting the biometriccomparison only to the data file associated with a particular PINnumber. Thus, by inputting a PIN, the biometric algorithm is able tonarrow its search within a much larger database to only one individualThe disadvantage to this arrangement is of course the loss of the purebiometric architecture coupled with the inconvenience of having toadminister and remember PIN numbers or maintain magnetic cards. In orderfor Biometric security systems to be unconditionally accepted by themarketplace, they must replace the more conventional security methods inbiometric-only embodiments.

Iris and retina identification systems, although very accurate, areconsidered “invasive”, expensive and not practical for applicationswhere limited computer memory storage is available. Voice recognition issomewhat less invasive, however it can require excessive memory storagespace for the various voice “templates” and sophisticated recognitionalgorithms. All three of these technologies have processing delaysassociated with them that make their use in one-to-many verificationapplications inappropriate.

Face verification systems, although non-invasive with minimal processingdelays, tend to be less accurate than the methods described above. Facerecognition systems can be successfully implemented for one-to-manyverification applications, however, because recognition algorithms suchas principal component analysis exist which permit extremely rapidsearches and ordering of large databases of facial images. Due to theabundant availability of extremely fast and inexpensive microprocessors,it is not difficult to create algorithms capable of searching throughmore than 20,000 facial images in less than one second.

Fingerprint verification is a minimally invasive and highly accurate wayto identify an individual A fingerprint verification system utilizing anintegrated circuit or optically based sensor can typically scan througha large database of users at the rate of approximately one comparisonper 100 milliseconds. Although this delay is acceptable for smallnumbers of users, delays of several seconds can be incurred when thenumber of users exceeds 20 to 30 individuals. For example, for anextremely large user database of 2000 individuals and assuming 100milliseconds processing delay per individual, a worst-case verificationdelay could be more than three minutes. This delay would clearly beunacceptable in all but the most tolerant security applications.

The prior references are abundant with biometric verification systemsthat have attempted to identify an individual based on one or morephysiologic metrics. Some inventors have combined more than onebiometric system in an attempt to increase overall accuracy of theverification event. One of the major problems that continues to impedethe acceptance of biometric verification systems is unacceptable delaysassociated with one-to-many verification events. To date, the onlyattempt directed towards reducing these unacceptable delays forbiometric systems has been to add a non-biometric discriminator thatconverts one-to-many verification tasks to one-to-one. Althougheffective, combining biometric and non-biometric systems is notdesirable for the reasons stated herein above.

Although many inventors have devised myriad approaches attempting toprovide inexpensive, minimally invasive, and fast fingerprintverification systems in which fingerprints of human users could bestored, retrieved and compared at some later time to verify that a humanuser is indeed a properly authorized user, none have succeeded inproducing a system that is practical and desirable for use in securityapplications requiring one-to-many biometric verification. Because ofthese and other significant imitations, commercially viablebiometric-based security systems have slow in coming to market.

The present invention overcomes all of the aforesaid imitations bycombining a very fast and streamlined facial image-based search enginewith state-of-the-art fingerprint verification algorithms. The presentinvention allows fingerprint verification analysis to be utilized inone-to-many applications by first reducing the problem to one-to-few.The facial image-based search engine can rapidly order a user databasewhich then permits the fingerprint verification engine to search in aheuristic fashion. Often, after a database has been so organized basedon facial image recognition, less than 10 fingerprint comparisons arenecessary to find the authorized user. In reference to the exampledescribed herein above, even with a 2000 user database, the fingerprintalgorithm would only need to compare ten individual fingerprints to finda match. Thus instead of a 3 minute processing delay, any givenindividual in the database would likely only experience a one secondprocessing delay. This novel utilization of one biometric to provide aheuristic search method for another biometric allows the creation of atruly practical “pure” biometric security system

SUMMARY OF THE INVENTION

It is an object of the present invention to improve the apparatus andmethod for providing biometric security.

It is another object of the present invention to improve the apparatusand method for verifying an individual fingerprint of a human user in alarge database of users.

Accordingly, one embodiment of the present invention is directed to afingerprint verification system utilizing a facial image-based heuristicsearch method which includes a first computer-based device having storedthereon encoded first human fingerprint biometric data representative ofan authorized human user, an integrated circuit-based or optically-basedfingerprint sensor for gathering said first fingerprint data, a controldevice with display and keyboard for enrolling said authorized humanuser, a second computer-based device located remotely from said firstcomputer-based device for providing verification of said authorizedhuman user, a network for communicating data between said firstcomputer-based device and said second computer-based device, areceptacle or the like associated with said second computer-based devicehaving embedded therein an integrated circuit-based or optically-basedfingerprint sensor for real-time gathering of second human fingerprintbiometric data, a video camera and digitizer associated with said secondcomputer-based device for real-time gathering of human facial imagedata, and software resident within said second computer-based device,which can include minutiae analysis, principal component analysis,neural networks or other equivalent algorithms, for comparing said firsthuman biometric data with said second human biometric data and producingan output signal therefrom for use in the verification of said humanuser. The apparatus may optionally include an electronic interface forcontrolling a security system which can be enabled or disabled based onwhether or not said human user's biometric data is verified by saidbiometric verification algorithms.

Another embodiment of the present invention is directed to a method forlayering biometrics wherein facial image recognition is utilized in aone-to-many mode to order the said user database enabling a heuristicsearch for fingerprint matching, and subsequently re-utilizing facialimage verification in a one-to-one mode to confirm the fingerprintverification. This arrangement has the dual advantage of decreasingprocessing delay and increasing overall security by ameliorating falseacceptance rates for unauthorized users. The method is furthercharacterized as using a computer-based device to perform the steps ofthe invention.

Other objects and advantages will be readily apparent to those ofordinary skill in the art upon viewing the drawings and reading thedetailed description hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram of an aspect of the present invention forthe verification of fingerprints utilizing a facial image-basedheuristic search method.

FIG. 2 shows in flow diagram a representation of the general processingsteps of the present invention.

FIG. 3 shows in functional block diagram a representation of a neuralnetwork of the present invention.

FIG. 4 shows in functional block diagram a representation of principalcomponent analysis (PCA) of the present invention.

FIG. 5 shows a representation of a human facial image transformation ofthe present invention.

FIG. 6 shows exemplar steps utilized by the face recognition softwareengine in preprocessing facial image data prior torecognition/identification.

FIG. 7 shows in functional block diagram a representation of minutiaeanalysis of the present invention.

DETAILED DESCRIPTION

Although those of ordinary skill in the art will readily recognize manyalternative embodiments, especially in light of the illustrationsprovided herein, this detailed description is of the preferredembodiment of the present invention, an apparatus and method forproviding fingerprint verification utilizing a facial image-basedheuristic search paradigm, the scope of which is limited only by theclaims appended hereto.

As particularly shown in FIG. 1, an apparatus for providing fingerprintverification of the present invention is referred to by the numeral 100and generally comprises a client terminal with associated processingelements and interface electronics 101, an administrative control centerand server 102, a communications network for communicating data betweenthe local computer and administrative control center which can include alocal area network (LAN) and the Internet 103, and biometric userinterface which encloses a fingerprint sensor, and video camera 104.

Referring now to FIG. 1, an apparatus for providing fingerprintverification utilizing a facial image-based heuristic search methodincludes a local computer 113 having a central processor (CP) 116 wellknown in the art and commercially available under such trademarks as“Intel® 486”, “Pentium®” and “Motorola 68000”, conventional non-volatileRandom Access Memory (RAM) 114, conventional Read Only Memory (ROM) 115,disk storage device 118, video digitizer circuit board 110 fordigitizing facial image data, and sensor interface electronics 119 forcommunicating digitized fingerprint data and digital control datatherethrough. An optical, capacitive or thermal-based fingerprint sensor120, which can be one of many well known to anyone of ordinary skill inthe art and commercially available under such trademarks as VeridicomOpenTouch™, Thomson FingerChip™, Digital Persona U.are.U™ and AuthenTecInc. FingerLoc™, and a video camera 112 which is well known to anyone ofordinary skill in the art is enclosed in biometric user interface 104.Optional access control electronics 153 electrically associated withsensor interface electronics 119 for actuating an electromechanical lockinterface 154, such as an electric strike plate which is commonlyutilized in myriad security applications and is well known to anyone ofordinary skill in the art is provided to optionally control access to afacility, computer system or a financial transaction. Access controlelectronics 153 provides access only when a signal indicative ofrecognition of an authorized human user is received from local computer113. A communications cable 158, well known to anyone of ordinary skillin the art, is provided to facilitate the communication of video signalsfrom video camera 112 to video digitizer 110 and digital fingerprintdata from fingerprint sensor 120 to sensor interface electronics 119.Communications cable 158 is further characterized as providingelectrical power to biometric user interface 104 and digital controlsignals from sensor interface electronics 119 to access controlelectronics 153. Fingerprint sensor 120 is situated within the biometricuser interface 104 in such a way as to facilitate intimate contactbetween a thumb or finger of human user 150. The local computer 113 hasoperably associated therewith facial image matching algorithm 140 whichrank orders the biometric database stored on said disk storage device118 and fingerprint verification software 141 which compares a firstdigitized human fingerprint 151, stored on said disk storage device 118with a second digitized human fingerprint 152 acquired in real-time fromhuman user 150 and provides a signal indicative of verification ornon-verification of human user 150. The facial image matching algorithm140 can be one of several algorithms known by anyone who is of ordinaryskill in the art such as neural networks 300 or principal componentanalysis 400. The fingerprint verification software 141 can be of one ofseveral algorithms known by anyone who is of ordinary skill in the artsuch as minutiae analysis 700 or another equivalent algorithm, theparticulars of which are further described hereinafter.

An administrative control center 102 comprised of server 121, videomonitor 128, keypad 129, all of which can be selected from myriadoff-the-shelf components well known to anyone of ordinary skill in theart, and an optical, capacitive or thermal-based fingerprint sensor 130,which can be one of many well known to anyone of ordinary skill in theart and commercially available under such trademarks as VeridicomOpenTouch™, Thomson FmgerChip™, Digital Persona U.are.U™ and AuthenTecInc. FingerLoc™, is provided as means for the enrollment of anauthorized first digitized human fingerprint(s) 151 of human user 150.Although the preferred embodiment of the present invention 100 makes useof a conventional keyboard and personal identification code to provide asecure barrier against unauthorized introduction of surreptitious users,the administrative control center 102 may comprise any hardware orsoftware barrier performing the equivalent function. For example, atouch pad or cipher lock may be used in other embodiments.Administrative control center 102 is preferably located in a secure areaand is remotely connectable to one or more client terminals 101 via acommunications network for communicating data between the local computerand administrative control center which can include a LAN and theInternet 103.

Server 121 is further characterized as having a central processor (CP)122 well known in the art and commercially available under suchtrademarks as “Intel® 486”, “Pentium®” and “Motorola 68000”,conventional non-volatile Random Access Memory (RAM) 123, conventionalRead Only Memory (ROM) 124, disk storage device 125, and fingerprintsensor interface electronics 126 for communicating digitized fingerprintdata acquired from fingerprint sensor 130. Administrative controlsoftware 127 resident within server 121 is responsible for enrolling newusers, deleting old users and generally managing and maintaining themaster biometric database 132. The master biometric database 132 residesin fixed disk storage device 125.

Referring now particularly to FIG. 2, a method for verifying afingerprint of a human user utilizing a facial image-based heuristicsearch algorithm designated by the numeral 200 begins with the step ofenrolling an authorized fingerprint(s) 201 via the administrativecontrol center and server 102. This first digitized fingerprint data 151is stored in the master biometric database 132 of server 121. Next, themaster biometric database 132 is distributed 202 to each of the remoteclient terminals 101 where the data is stored in a local biometricdatabase 131 in disk storage device 118 and subsequently utilized duringthe authentication step. Facial images, utilized in organizing the localbiometric database 131 to allow efficient fingerprint matching, are notgathered during the initial enrollment of an authorized human user 150,but are gathered at the remote client terminals 101 when the individualterminal is accessed for the first time. This approach enables thesystem to compensate for lighting variations and differences in thevideo cameras 112 and other factors related to installation variances.In addition, these reference faces can be updated at the remote clientterminals 101 when required such as when a human user grows a beard ormoustache or otherwise significantly alters his/her facial appearance.

When a human user 150 attempts, for example, to enter a secure arearequiring biometric authentication, upon approaching the biometric userinterface 104 video camera 112, as described in detail herein above,senses motion in its field of view which triggers the authenticationevent 203. Upon triggering authentication event 203, software residentlocal computer 113 finds and tracks 204 any facial images present withinthe digitized video image. This face finding/tracking 204 step isnecessary to ensure that the motion is caused by a genuine human faceand not the product of an artifact such as an arm or hand. Localcomputer 113 digitizes several facial images and stores them in RAMmemory 114. A facial image preprocessing algorithm 205, described indetail hereinafter, is subsequently utilized to normalize, orient andselect the highest quality facial images to enable the heuristicordering step 206, to search and organize the local biometric database131 in the order of best-to-worst facial image match. The presentinvention 100 utilizes a facial image matching algorithm 140 which canbe neural networks 300 or principal component analysis 400 as describedin detail herein below.

If the authorized human user 150 is accessing the client terminal 101for the first time since enrolling through the administrative controlcenter and server 102, a heuristic ordering 206 would not be possiblebecause facial images associated with said human user would not havepreviously been stored. For this case, client terminal 101 wouldassociate and store the highest quality facial image of human user 150with the appropriate data file in the local biometric database 131whereupon a heuristic ordering 206 can subsequently be performed eachtime the human user 150 attempts to gain access at the client terminal101 thereafter. The heuristic ordering step 206 is capable of sorting alarge local biometric database 131 very quickly. For example, thepresent invention 100 utilizing principal component analysis 400 iscapable of scanning approximately 20,000 facial images per second andarranging them in their proper order. Typically, principal componentanalysis 400 can narrow the search for a human user 150 to ten or fewerfaces, i.e., human user 150 can be found in the first ten data entriesof the re-organized local biometric database 131.

Once the facial images and their associated first digitized fingerprintdata 151 have been properly ordered, the human user 150 touchesfingerprint sensor 120 with the previously enrolled thumb or fingerwhereupon real-time second digitized fingerprint data 152 is acquired207 and stored in RAM memory 114 of local computer 113. Next, the localcomputer 113 implements a one-to-one fingerprint verification step 208whereupon each of the first digitized fingerprint data 151 in theheuristically ordered local biometric database 131 is compared to thesecond digitized fingerprint data 152 acquired in step 207. The presentinvention 100 utilizes a fingerprint verification algorithm 141, whichcan be minutiae analysis 700, or an equivalent algorithm as described indetail herein below.

The fingerprint verification algorithm 141 typically compares twodigitized fingerprints in 100 milliseconds and has a false acceptancerate (FAR) of approximately 1 in 10,000. Although highly accurate, thealgorithm 141 is not efficient for searches involving large databasesand could take up to three minutes to search through 2000 individualfingerprints. The present invention 100 overcomes this efficiencylimitation by, employing step 206 which utilizes facial image sorting toorder the local biometric database 131 in its most efficient form Afterstep 206 has been completed, the present invention 100 typically locatesand verifies the fingerprint 152 of human user 150 in 500 millisecondson average regardless of the size of the local biometric database.

Finally, if the second digitized fingerprint data 152 acquired in step207 is found to match within a predetermined certainty the firstdigitized fingerprint data 151 verified in step 208, a signal indicativeof verification is generated 210 which can then be utilized to actuatean electric lock 154 or permit access to a computer account or financialtransaction as described herein above.

In an alternative embodiment utilizing layered biometrics of the presentinvention 100 an optional facial image verification step 209 can beemployed subsequent the one-to-one fingerprint verification step 208 asdescribed herein above. Although facial image verification algorithmstypically have a FAR between 1 in 200 and 1 in 1,000 and are notgenerally suited for accurate verification of a human user, systemperformance can be significantly enhanced by combining facialverification with another more reliable verification algorithm such asthe fingerprint verification algorithm 141. The layering of the twoalgorithms in a multiple biomteric configuration can yield asignificantly higher FAR than can be obtained by either algorithm alone.For example, with the present invention 100, the addition of step 209subsequent step 208 would yield a FAR approximately equal to 1 in 200multiplied by 1 in 10,000 or 1 in 2,000,000. Step 209 utilizes the samefacial image matching algorithm 140 which can be neural networks 300 orprincipal component analysis 400 as described herein below and asutilized in the heuristic ordering step 206. When principal componentanalysis 400 is utilized in comparing two images in a one-to-one mode,the algorithm generates a scalar error with a magnitude that isindicative of the quality of match between the two digitized facialimages. A threshold for verification can be preselected so that onlymatch errors below said threshold, generated by the facial imagematching algorithm 140, would produce a signal indicative ofverification. The heuristic sorting step 206 of the present invention100 uses this error to rank order the facial images from best to worstmatch.

There are a variety of methods by which the facial image-based heuristicsearch element of the present invention 100 can be implemented. Althoughthe methods differ in computational structure, it is widely acceptedthat they are functionally equivalent. An example of two practicaltechniques, neural networks 300 and principal component analysis 400,are provided hereinbelow and are depicted in FIG. 3 and FIG. 4respectively.

As shown in FIG. 3, the neural network 300 includes at least one layerof trained neuron-like units, and preferably at least three layers. Theneural network 300 includes input layer 370, hidden layer 372, andoutput layer 374. Each of the input layer 370, hidden layer 372, andoutput layer 374 include a plurality of trained neuron-like units 376,378 and 380, respectively.

Neuron-like units 376 can be in the form of software or hardware. Theneuron-like units 376 of the input layer 370 include a receiving channelfor receiving human facial image data 171, and comparison facial imagedata 169 wherein the receiving channel includes a predeterminedmodulator 375 for modulating the signal.

The neuron-like units 378 of the hidden layer 372 are individuallyreceptively connected to each of the units 376 of the input layer 370.Each connection includes a predetermined modulator 377 for modulatingeach connection between the input layer 370 and the hidden layer 372.

The neuron-like units 380 of the output layer 374 are individuallyreceptively connected to each of the units 378 of the hidden layer 372.Each connection includes a predetermined modulator 379 for modulatingeach connection between the hidden layer 372 and the output layer 374.Each unit 380 of said output layer 374 includes an outgoing channel fortransmitting the output signal.

Each neuron-like unit 376, 378, 380 includes a dendrite-like unit 360,and preferably several, for receiving incoming signals. Eachdendrite-like unit 360 includes a particular modulator 375, 377, 379which modulates the amount of weight which is to be given to theparticular characteristic sensed as described below. In thedendrite-like unit 360, the modulator 375, 377, 379 modulates theincoming signal and subsequently transmits a modified signal 362. Forsoftware, the dendrite-like unit 360 comprises an input variable X_(a)and a weight value W_(a) wherein the connection strength is modified bymultiplying the variables together. For hardware, the dendrite-like unit360 can be a wire, optical or electrical transducer having a chemically,optically or electrically modified resistor therein.

Each neuron-like unit 376, 378, 380 includes a soma-like unit 363 whichhas a threshold barrier defined therein for the particularcharacteristic sensed. When the soma-like unit 363 receives the modifiedsignal 362, this signal must overcome the threshold barrier whereupon aresulting signal is formed. The soma-like unit 363 combines allresulting signals 362 and equates the combination to an output signal364 indicative of the caliber of match for a human facial image.

For software, the soma-like unit 363 is represented by the sumα=Σ_(a)X_(a)W_(a)−β, where β is the threshold barrier. This sum isemployed in a Nonlinear Transfer Function (NTF) as defined below. Forhardware, the soma-like unit 363 includes a wire having a resistor; thewires terminating in a common point which feeds into an operationalamplifier having a nonlinear component which can be a semiconductor,diode, or transistor.

The neuron-like unit 376, 378, 380 includes an axon-like unit 365through which the output signal travels, and also includes at least onebouton-like unit 366, and preferably several, which receive the outputsignal from the axon-like unit 365. Bouton/dendrite linkages connect theinput layer 370 to the hidden layer 372 and the hidden layer 372 to theoutput layer 374. For software, the axon-like unit 365 is a variablewhich is set equal to the value obtained through the NTF and thebouton-like unit 366 is a function which assigns such value to adendrite-like unit 360 of the adjacent layer. For hardware, theaxon-like unit 365 and bouton-like unit 366 can be a wire, an optical orelectrical transmitter.

The modulators 375, 377, 379 which interconnect each of the layers ofneurons 370, 372, 374 to their respective inputs determines the matchingparadigm to be employed by the neural network 300. Human facial imagedata 171, and comparison facial image data 169 are provided as inputs tothe neural network and the neural network then compares and generates anoutput signal in response thereto which is one of the caliber of matchfor the human facial image.

It is not exactly understood what weight is to be given tocharacteristics which are modified by the modulators of the neuralnetwork, as these modulators are derived through a training processdefined below.

The training process is the initial process which the neural networkmust undergo in order to obtain and assign appropriate weight values foreach modulator. Initially, the modulators 375, 377, 379 and thethreshold barrier are assigned small random non-zero values. Themodulators can each be assigned the same value but the neural network'slearning rate is best maximized if random values are chosen. Humanfacial image data 171 and comparison facial image data 169 are fed inparallel into the dendrite-like units of the input layer (one dendriteconnecting to each pixel in facial image data 171 and 169) and theoutput observed.

The Nonlinear Transfer Function (NTF) employs a in the followingequation to arrive at the output:NTF=1/[1+e ^(−α)]

For example, in order to determine the amount weight to be given to eachmodulator for any given human facial image, the NTF is employed asfollows:

If the NTF approaches 1, the soma-like unit produces an output signalindicating a strong match. If the NTF approaches 0, the soma-like unitproduces an output signal indicating a weak match.

If the output signal clearly conflicts with the known empirical outputsignal, an error occurs. The weight values of each modulator areadjusted using the following formulas so that the input data producesthe desired empirical output signal.

For the output layer:

W*_(kol)=W_(kol)+GE_(k)Z_(kos)

W*_(kol)=new weight value for neuron-like unit k of the outer layer.

W_(kol)=current weight value for neuron-like unit k of the outer layer.

G=gain factor

Z_(kos)=actual output signal of neuron-like unit k of output layer.

D_(kos)=desired output signal of neuron-like unit k of output layer.

E_(k)=Z_(kos) (1−Z_(kos))(D_(kos)−Z_(kos)), (this is an error termcorresponding to neuron-like unit k of outer layer).

For the hidden layer:

W*_(jhl)=W_(jhl)+GE_(j)Y_(jos)

W*_(jhl)=new weight value for neuron-like unit j of the hidden layer.

W_(jhl)=current weight value for neuron-like unit j of the hidden layer.

G=gain factor

Y_(jos)=actual output signal of neuron-like unit j of hidden layer.

E_(j)=Y_(jos)(1−Y_(jos))Σ_(k)(Σ_(k)ΣW_(kol)), (this is an error termcorresponding to neuron-like unit j of hidden layer over all k units).

For the input layer:

W*_(iil)=W_(iil)+GE_(i)X_(ios)

W*_(iil)=new weight value for neuron-like unit I of input layer.

W_(iil)=current weight value for neuron-like unit I of input layer.

G=gain factor

X_(ios)=actual output signal of neuron-like unit I of input layer.

E_(i)=X_(ios)(1−X_(ios))Σ_(j)(E_(j)·W_(jhl)), (this is an error termcorresponding to neuron-like unit i of input layer over all j units).

The training process consists of entering new (or the same) exemplardata into neural network 300 and observing the output signal withrespect to a known empirical output signal. If the output is in errorwith what the known empirical output signal should be, the weights areadjusted in the manner described above. This iterative process isrepeated until the output signals are substantially in accordance withthe desired (empirical) output signal, then the weight of the modulatorsare fixed.

Upon fixing the weights of the modulators, predetermined face-spacememory indicative of the caliber of match are established. The neuralnetwork is then trained and can make generalizations about human facialimage input data by projecting said input data into face-space memorywhich most closely corresponds to that data.

The description provided for neural network 300 as utilized in thepresent invention is but one technique by which a neural networkalgorithm can be employed. It will be readily apparent to those who areof ordinary skill in the art that numerous neural network model typesincluding multiple (sub-optimized) networks as well as numerous trainingtechniques can be employed to obtain equivalent results to the method asdescribed herein above.

Referring now particularly to FIG. 4, and according to a secondpreferred embodiment of the present invention, a principal componentanalysis (PCA) may be implemented as the system's facial image matchingalgorithm 140. The PCA facial image matching/verification elementgenerally referred to by the numeral 400, includes a set of trainingimages 481 which consists of a plurality of digitized human facial imagedata 171 representative of a cross section of the population of humanfaces. In order to utilize PCA in facial image recognition/verificationa Karhunen-Love Transform (KLT), readily known to those of ordinaryskill in the art, can be employed to transform the set of trainingimages 481 into an orthogonal set of basis vectors or eigenvectors. Inthe present invention, a subset of these eigenvectors, calledeigenfaces, comprise an orthogonal coordinate system, detailed furtherherein, and referred to as face-space.

The implementation of the KLT is as follows: An average facial image482, representative of an average combination of each of the trainingimages 481 is first generated. Next, each of the training images 481 aresubtracted from the average face 482 and arranged in a two dimensionalmatrix 483 wherein one dimension is representative of each pixel in thetraining images, and the other dimension is representative of each ofthe individual training images. Next, the transposition of matrix 483 ismultiplied by matrix 483 generating a new matrix 484. Eigenvalues andeigenvectors 485 are thenceforth calculated from the new matrix 484using any number of standard mathematical techniques that will be wellknown by those of ordinary skill in the art such as Jacobi's method.Next, the eigenvalues and eigenvectors 485 are sorted 486 from largestto smallest whereupon the set is truncated to only the first severaleigenvectors 487 (e.g. between 5 and 20 for acceptable performance).Lastly, the truncated eigenvalues and eigenvectors 487 are provided asoutputs 488. The eigenvalues and eigenvectors 488 and average face 482can then be stored inside the RAM memory 114 in the local computer 113for use in recognizing or verifying facial images.

Referring now to FIG. 5, for the PCA algorithm 400 facial imagematching/verification is accomplished by first finding and converting ahuman facial image to a small series of coefficients which representcoordinates in a face-space that are defined by the orthogonaleigenvectors 488. Initially a preprocessing step, defined further hereinbelow, is employed to locate, align and condition the digital videoimages. Facial images are then projected as a point in face-space. Thecaliber of match for any human user 150 is provided by measuring theEuclidean distance between two such points in face-space. In addition,if the coefficients generated as further described below representpoints in face-space that are within a predetermined acceptancedistance, a signal indicative of verification is generated. If; on theother hand, the two points are far apart, a signal indicative onnon-verification is generated. Although this method is given as aspecific example of how the PCA 400 algorithm works, the mathematicaldescription and function of the algorithm is equivalent to that of theneural network 300 algorithm The projection of the faces into face-spaceis accomplished by the individual neurons and hence the abovedescription accurately relates an analogous way of describing theoperation of neural network 300.

Again using the PCA 400 algorithm as an example, a set of coefficientsfor any given human facial image is produced by taking the digitizedhuman facial image 171 of a human user 150 and subtracting 590 theaverage face 482. Next, the dot product 591 between the difference imageand one eigenvector 488 is computed by dot product generator 592. Theresult of the dot product with a single eigenface is a numerical value593 representative of a single coefficient for the image 171. Thisprocess is repeated for each of the set of eigenvectors 488 producing acorresponding set of coefficients 594 which can then be stored 595 inthe disk storage device 118 operably associated with local computer 113described herein above. Because there are relatively few coefficientsnecessary to represent a set of reference faces of a single human user150, the storage space requirements are minimal and on the order of 100bytes per stored encoded facial image.

As further described below, said first human facial images of a humanuser 150 are stored in disk storage device 118 during the trainingprocess. Each time the facial image of human user 150 is acquired by thevideo camera 112 thereafter, a said second human facial image of saidhuman user 150 is acquired, the facial image is located, aligned,processed and compared to every said first human facial image in thedatabase by PCA 400 or neural network 300. Thus, the technique asdescribed above provides the means by which two said facial image setscan be accurately compared and a matching error signal can be generatedtherefrom The preferred method of acquiring and storing the aforesaidfacial images of said human user, begins with the human user 150,providing one and preferably four to eight facial images of him/herselfto be utilized as templates for all subsequent sorting or verificationevents. To accomplish this, said authorized human user approaches thebiometric user interface 104 and touches fingerprint sensor 120. If nofacial images have been previously stored, or if the facialcharacteristics of human user 150 have changed significantly, localcomputer 113 performs an exhaustive search of the local biometricdatabase 131 to verify the identity of said human user based on thefingerprint only. Once the individual is verified, local computer 113enters a “learning mode” and subsequently acquires several digitizedfirst human facial images of the human user 150 through the use of CCDvideo camera 112 and digitizer 110. These first human facial images arepreprocessed, the highest quality images selected and thenceforthreduced to coefficients and stored in the disk storage device 118 oflocal computer 113. These selected fist human facial images will beutilized thereafter as the reference faces. Thereafter when saidauthorized human user 150 approaches biometric user interface 104 toinitiate a biometric verification sequence, the human user 150 trigger'smotion detection and face finding algorithms incorporated in the facialimage matching algorithm 140 as described in detail herein below. Atthis time, video camera 112 begins acquiring second human facial imagesof the human user 150 and converts said second human facial images todigital data via digitizer 110. The digitized second human facial imagesobtained thereafter are stored in the RAM memory 114 of computer 113 ascomparison faces.

Once the said second human facial image(s) has been stored in computer113, the facial image matching algorithm 140, either neural network 300or PCA 400 can be employed to perform a comparison between said storedfirst human facial image and said acquired second human facial image andproduce an output signal in response thereto indicative of caliber ofmatch of the human user 150.

As previously stated herein above, and referring now to FIG. 6, apreprocessing function 600 must typically be implemented in order toachieve efficient and accurate processing by the chosen facial imagematching algorithm 140 of acquired human facial image data 171. Whetherutilizing a neural network 300, PCA 400 or another equivalent facerecognition software algorithm, the preprocessing function generallycomprises elements adapted for (1) face finding 601, (2) featureextraction 602, (3) determination of the existence within the acquireddata of a human facial image 603, (4) scaling, rotation, translation andpre-masking of the captured human image data 604, and (5) contrastnormalization and final masking 605. Although each of thesepreprocessing function elements 601, 602, 603, 604, 605 is described indetail further herein, those of ordinary skill in the art will recognizethat some or all of these elements may be dispensed with depending uponthe complexity of the chosen implementation of the facial image matchingalgorithm 140 and desired overall system attributes.

In the initial preprocessing step of face finding 601, objectsexhibiting the general character of a human facial image are locatedwithin the acquired image data 171 where after the general location ofany such existing object is tracked. Although those of ordinary skill inthe art will recognize equivalent alternatives, three exemplary facefinding techniques are (1) baseline subtraction and trajectory tracking,(2) facial template subtraction, or the lowest error method, and (3)facial template cross-correlation.

In baseline subtraction and trajectory tracking, a first, or baseline,acquired image is generally subtracted, pixel value-by-pixel value, froma second, later acquired image. As will be apparent to those of ordinaryskill in the art, the resulting difference image will be a zero-valueimage if there exists no change in the second acquired image withrespect to the first acquired image. However, if the second acquiredimage has changed with respect to the first acquired image, theresulting difference image will contain nonzero values for each pixellocation in which change has occurred. Assuming that a human user 150will generally be non-stationary with respect to the system's camera112, and will generally exhibit greater movement than any backgroundobject, the baseline subtraction technique then tracks the trajectory ofthe location of a subset of the pixels of the acquired imagerepresentative of the greatest changes. During initial preprocessing601, 602, this trajectory is deemed to be the location of a likely humanfacial image.

In facial template subtraction, or the lowest error method, a ubiquitousfacial image, i.e. having only nondescript facial features, is used tolocate a likely human facial image within the acquired image data.Although other techniques are available, such a ubiquitous facial imagemay be generated as a very average facial image by summing a largenumber of facial images. According to the preferred method, theubiquitous image is subtracted from every predetermined region of theacquired image, generating a series of difference images. As will beapparent to those of ordinary skill in the art, the lowest error indifference will generally occur when the ubiquitous image is subtractedfrom a region of acquired image data containing a similarly featuredhuman facial image. The location of the region exhibiting the lowesterror, deemed during initial preprocessing 601, 602 to be the locationof a likely human facial image, may then be tracked.

In facial template cross-correlation, a ubiquitous image iscross-correlated with the acquired image to find the location of alikely human facial image in the acquired image. As is well known tothose of ordinary skill in the art, the cross-correlation function isgenerally easier to conduct by transforming the images to the frequencydomain, multiplying the transformed images, and then taking the inversetransform of the product. A two-dimensional Fast Fourier Transform(2D-FFT), implemented according to any of myriad well known digitalsignal processing techniques, is therefore utilized in the preferredembodiment to first transform both the ubiquitous image and acquiredimage to the frequency domain. The transformed images are thenmultiplied together. Finally, the resulting product image istransformed, with an inverse FFT, back to the time domain as thecross-correlation of the ubiquitous image and acquired image. As isknown to those of ordinary skill in the art, an impulsive area, orspike, will appear in the cross-correlation in the area of greatestcorrespondence between the ubiquitous image and acquired image. Thisspike, deemed to be the location of a likely human facial image, is thentracked during initial preprocessing 601, 602.

Once the location of a likely human facial image is known, featureidentification 602 is employed to determine the general characteristicsof the thought-to-be human facial image for making a thresholdverification that the acquired image data contains a human facial imageand in preparation for image normalization. Feature identificationpreferably makes use of eigenfeatures, generated according to the sametechniques previously detailed for generating eigenfaces, to locate andidentify human facial features such as the eyes, nose and mouth. Therelative locations of these features are then evaluated with respect toempirical knowledge of the human face, allowing determination of thegeneral characteristics of the thought-to-be human facial image as willbe understood further herein. As will be recognized by those of ordinaryskill in the art, templates may also be utilized to locate and identifyhuman facial features according to the time and frequency domaintechniques described for face finding 601.

Once the initial preprocessing function elements 601, 602 have beenaccomplished, the system is then prepared to make an evaluation 603 asto whether there exists a facial image within the acquired data, i.e.whether a human user 150 is within the field of view of the system'scamera 112. According to the preferred method, the image data is eitheraccepted or rejected based upon a comparison of the identified featurelocations with empirical knowledge of the human face. For example, it isto be generally expected that two eyes will be found generally above anose, which is generally above a mouth. It is also expected that thedistance between the eyes should fall within some range of proportion tothe distance between the nose and mouth or eyes and mouth or the like.Thresholds are established within which the location or proportion datamust fall in order for the system to accept the acquired image data ascontaining a human facial image. If the location and proportion datafalls within the thresholds, preprocessing continue. If, however, thedata falls without the thresholds, the acquired image is discarded.

Threshold limits may also be established for the size and orientation ofthe acquired human facial image in order to discard those images likelyto generate erroneous recognition results due to poor presentation ofthe user 150 to the system's camera 112. Such errors are likely to occurdue to excessive permutation, resulting in overall loss of identifyingcharacteristics, of the acquired image in the morphological processing604, 605 required to normalize the human facial image data, as detailedfurther herein. Applicant has found that it is simply better to discardborderline image data and acquire a new better image. For example, thesystem 100 may determine that the image acquired from a user 150 lookingonly partially at the camera 112, with head sharply tilted and at alarge distance from the camera 112, should be discarded in favor ofattempting to acquire a better image, i.e. one which will require lesspermutation 604, 605 to normalize. Those of ordinary skill in the artwill recognize nearly unlimited possibility in establishing the requiredthreshold values and their combination in the decision making process.The final implementation will be largely dependent upon empiricalobservations and overall system implementation.

Although the threshold determination element 603 is generally requiredfor ensuring the acquisition of a valid human facial image prior tosubsequent preprocessing 604, 605 and eventual attempts by the facialimage matching algorithm 140 to verify 606 the recognition status of auser 150, it is noted that the determinations made may also serve toindicate a triggering event condition. As previously stated, one of thepossible triggering event conditions associated with the apparatus isthe movement of a user 150 within the field of view of the system'scamera 112. Accordingly, much computational power may be conserved bydetermining the existence 603 of a human facial image as a preprocessingfunction—continuously conducted as a background process. Once verifiedas a human facial image, the location of the image within the field ofview of the camera 112 may then be relatively easily monitored by thetracking functions detailed for face finding 601. The system 100 maythus be greatly simplified by making the logical inference that anidentified known user 150 who has not moved out of sight, but who hasmoved, is the same user 150.

After the system 100 determines the existence of human facial imagedata, and upon triggering of a matching/verification event, the humanfacial image data is scaled, rotated, translated and pre-masked 604, asnecessary. Applicant has found that the various facial image matchingalgorithms 140 perform with maximum efficiency and accuracy if presentedwith uniform data sets. Accordingly, the captured image is scaled topresent to the facial image matching algorithm 140 a human facial imageof substantially uniform size, largely independent of the user'sdistance from the camera 112. The captured image is then rotated topresent the image in a substantially uniform orientation, largelyindependent of the user's orientation with respect to the camera 112.Finally, the captured image is translated to position the imagepreferably into the center of the acquired data set in preparation formasking, as will be detailed further herein. Those of ordinary skill inthe art will recognize that scaling, rotation and translation are verycommon and well-known morphological image processing functions that maybe conducted by any number of well known methods. Once the capturedimage has been scaled, rotated and translated, as necessary, it willreside within a generally known subset of pixels of acquired image data.With this knowledge, the captured image is then readily pre-masked toeliminate the background viewed by the camera 112 in acquiring the humanfacial image. With the background eliminated, and the human facial imagenormalized, much of the potential error can be eliminated in contrastnormalization 605, detailed further herein, and eventual matching 606 bythe facial image matching algorithm 140.

Because it is to be expected that the present invention 100 will beplaced into service in widely varying lighting environments, thepreferred embodiment includes the provision of a contrast normalization605 function for eliminating adverse consequences concomitant theexpected variances in user illumination. Although those of ordinaryskill in the art will recognize many alternatives, the preferredembodiment of the present invention 100 comprises a histogramspecification function for contrast normalization. According to thismethod, a histogram of the intensity and/or color levels associated witheach pixel of the image being processed is first generated. Thehistogram is then transformed, according to methods well known to thoseof ordinary skill in the art, to occupy a predetermined shape. Finally,the image being processed is recreated with the newly obtained intensityand/or color levels substituted pixel-by-pixel. As will be apparent tothose of ordinary skill in the art, such contrast normalization 605allows the use of a video camera 112 having very wide dynamic range incombination with a video digitizer ii 110 having very fine precisionwhile arriving at an image to be verified having only a manageablenumber of possible intensity and/or pixel values. Finally, because thecontrast normalization 605 may reintroduce background to the image, itis preferred that a final masking 605 of the image be performed prior tofacial image matching 606. After final masking, the image is ready formatching 606 as described herein above.

There are a variety of methods by which the fingerprint verificationelement of the present invention 100 can be implemented. Although themethods differ in computational structure, it is widely accepted thatthey are functionally equivalent. An example of one practical technique,minutiae analysis 700, is provided hereinbelow and is depicted in FIG.7.

As shown in FIG. 7, the minutiae analysis 700, appropriate forimplementation of the present invention 100 includes the steps ofminutiae detection 710, minutiae extraction 720 and minutia matching730. After a human fingerprint 151 (template) or 152 (target) has beenacquired and digitized as described in steps 201 and 207 herein above,local ridge characteristics 711 are detected. The two most prominentlocal ridge characteristics 711, called minutiae, are ridge ending 712and ridge bifurcation 713. Additional minutiae suitable for inclusion inminutiae analysis 700 exist such as “short ridge”, “enclosure”, and“dot” and may also be utilized by the present invention 100. A ridgeending 712 is defined as the point where a ridge ends abruptly. A ridgebifurcation 713 is defined as the point where a ridge forks or divergesinto branch ridges. A fingerprint 151, 152 typically contains about 75to 125 minutiae. The next step in minutiae analysis 700 of the presentinvention 100 involves identifying and storing the location of theminutiae 712, 713 utilizing a minutiae cataloging algorithm 714. Inminutiae cataloging 714, the local ridge characteristics from step 711undergo an orientation field estimation 715 in which the orientationfield of the input local ridge characteristics 711 are estimated and aregion of interest 716 is identified. At this time, individual minutiae712, 713 are located, and an X and Y coordinate vector representing theposition of minutiae 712, 713 in two dimensional space as well as anorientation angle 0 is identified for template minutiae 717 and targetminutiae 718. Each are stored 719 in random access memory (RAM) 114.

Next, minutiae extraction 720 is performed for each detected minutiaepreviously stored in step 719 above. Each of the stored minutiae 719 areanalyzed by a minutiae identification algorithm 721 to determine if thedetected minutiae 719 are one of a ridge ending 712 or ridge bifurcation713. The matching-pattern vectors which are used for alignment in theminutiae matching step 730, are represented as two-dimensional discretesignals that are normalized by the average inter-ridge distance. Amatching-pattern generator 722 is employed to produce standardizedvector patterns for comparison. The net result of the matching-patterngenerator 722 are minutiae matching patterns 723 and 724. With respectto providing verification of a fingerprint as required by the presentinvention 100, minutiae template pattern 723 is produced for theenrolled fingerprint 151 of human user 150 and minutiae target pattern724 is produced for the real-time fingerprint 152 of human user 150.

Subsequent minutiae extraction 720, the minutiae matching 730 algorithmdetermines whether or not two minutiae matching patterns 723, 724 arefrom the same finger of said human user 150. A similarity metric betweentwo minutiae matching patterns 723, 724 is defined and a thresholding738 on the similarity value is performed. By representing minutiaematching patterns 723, 724 as two-dimensional “elastic” point patterns,the minutiae matching 730 may be accomplished by “elastic” point patternmatching, as is understood by anyone of ordinary skill in the art, aslong as it can automatically establish minutiae correspondences in thepresence of translation, rotation and deformations, and detect spuriousminutiae and missing minutiae. An alignment-based “elastic” vectormatching algorithm 731 which is capable of finding the correspondencesbetween minutiae without resorting to an exhaustive search is utilizedto compare minutiae template pattern 723, with minutiae target pattern724. The alignment-based “elastic” matching algorithm 731 decomposes theminutiae matching into three stages: (1) An alignment stage 732, wheretransformations such as translation, rotation and scaling between atemplate pattern 723 and target pattern 724 are estimated and the targetpattern 724 is aligned with the template pattern 723 according to theestimated parameters; (2) A conversion stage 733, where both thetemplate pattern 723 and the target pattern 724 are converted to vectors734 and 735 respectively in the polar coordinate system; and (3) An“elastic” vector matching algorithm 736 is utilized to match theresulting vectors 734, 735 wherein the normalized number ofcorresponding minutiae pairs 737 is reported. Upon completion of thealignment-based “elastic” matching 731, a thresholding 738 is thereafteraccomplished. In the event the number of corresponding minutiae pairs737 is less than the threshold 738, a signal indicative ofnon-verification is generated by computer 113. Conversely, in the eventthe number of corresponding minutiae pairs 737 is greater than thethreshold 738, a signal indicative of verification is generated bycomputer 113. Either signal is communicated by computer 113 to interfaceelectronics 153 via communication cable 158 as described in detailherein above.

The above described embodiments are set forth by way of example and arenot for the purpose of limiting the scope of the present invention. Itwill be readily apparent to those or ordinary skill in the art thatobvious modifications, derivations and variations can be made to theembodiments without departing from the scope of the invention. Forexample, the facial image-based heuristic search algorithms describedherein above as either a neural network 300 or principal componentanalysis 400 could also be one of a statistical based system, templateor pattern matching, or even rudimentary feature matching whereby thefeatures of the facial images are analyzed. Similarly, the fingerprintverification algorithms described in detail above as minutiae analysis700 could be one of many other algorithms well known to anyone ofordinary skill in the art. Accordingly, the claims appended heretoshould be read in their fill scope including any such modifications,derivations and variations.

1. A biometric verification system for determining whether a person'sdetected biometric characteristics match any biometric templates storedon a database containing the biometric templates of a large number ofusers without relying on a non-biometric discriminator to convert aone-to-many verification task to a one-to-one verification task, thesystem comprising: a video camera and video digitizer for acquiring adigital representation of a human facial image; a biometric sensor foracquiring a digital representation of a human fingerprint; a processorassociated with said camera, digitizer and biometric sensor, saidprocessor being capable of processing signals from said camera,digitizer and biometric sensor; heuristic ordering software resident onsaid processor for comparing said digital representation of a humanfacial image with stored facial images in a database of facial images,generating a match confidence therefrom and rank-ordering said databasefrom highest to lowest match confidence; and verification softwareresident on said processor for comparing said digital representation ofa human fingerprint with stored fingerprints associated with saidrank-ordered database in the order created by the heuristic orderingsoftware to verify the identity of said person and provide an outputsignal indicative of recognition
 2. The verification system of claim 1,further comprising an electronic locking mechanism which can be enabledor disabled by said output signal.
 3. A heuristic search method forcontrolling access to secure areas including the steps of: for each of aplurality of authorized human users, enrolling a first digitizedfingerprint and a first digitized facial image representation of saidauthorized human user and storing said first digitized fingerprint andsaid first digitized facial image in a database memory; detecting when aperson attempts to gain access to said secure area and acquiring asecond digitized facial image and a second digitized fingerprint;comparing said second digitized facial image with the first digitizedfacial image representations of each of said plurality of authorizedhuman users to determine a first match confidence with each of saidplurality of authorized human users; ordering said database memory fromhighest first match confidence to lowest first match confidence;comparing said second digitized fingerprint with the first digitizedfingerprint of one or more of said plurality of authorized human usersin the order created by said ordering step to determine a second matchconfidence; and allowing or preventing access to said secure area in theevent the second match confidence falls above or below a predeterminedthreshold without relying on a non-biometric discriminator to convert aone-to-many verification task to a one-to-one verification task.
 4. Themethod of claim 3, which is further characterized to include the step ofallowing access only in the event of said first match confidence andsaid second match confidence falling above a predetermined threshold. 5.The method of claim 3, which is further characterized to include thestep of activating an electronic lock mechanism in the event said secondmatch confidence falls above a predetermined threshold.
 6. The method ofclaim 3, which is further characterized to include the step ofactivating an alarm system in the event said second match confidencefalls below a predetermined threshold.
 7. The method of claim 6, whichis further characterized to include the step of notifying a securitymonitoring service located at a central receiving station in the eventsaid second match confidence falls below a predetermined threshold. 8.The method of claim 7, which is further characterized to include thestep of transmitting said second digitized facial image to said centralreceiving station to enable a positive identification of said humanuser.
 9. A one-to-many biometric verification system utilizing facialimage comparisons to enhance the efficiency of a fingerprintverification analysis, the verification system comprising: a storagedevice containing a database of facial image templates and fingerprinttemplates acquired from a plurality of enrolled individuals, saiddatabase associating each facial image template with at least onecorresponding fingerprint template acquired from the same enrolledindividual; a camera for obtaining a newly acquired digitalrepresentation of a human facial image; a fingerprint sensor forobtaining a newly acquired digital representation of a humanfingerprint; a processor associated with said storage device, camera,and fingerprint sensor; and software resident on said processor forcomparing the newly acquired digital representation of a human facialimage with the stored facial image templates, identifying a subset ofmore than one, but less than all, of said stored facial image templatesthat most closely match the newly acquired digital representation of ahuman facial image, then comparing the newly acquired digitalrepresentation of a human fingerprint with the fingerprint templatesassociated with said subset, and producing an output signal indicativeof whether an enrolled individual is recognized; wherein the software isadapted to produce an output signal indicative of whether an enrolledindividual is recognized without relying on a non-biometricdiscriminator to convert a one-to-many verification task to a one-to-oneverification task.
 10. The verification system of claim 9, being furthercharacterized in that enrolled individuals are not provided withpersonal identification codes that must be provided to and evaluated bythe verification system to produce an output signal indicative ofwhether an enrolled individual is recognized.
 11. The verificationsystem of claim 9, being further characterized in that enrolledindividuals are not provided with personal identification cards thatmust be provided to and evaluated by the verification system to producean output signal indicative of whether an enrolled individual isrecognized.
 12. The verification system of claim 9, being furthercharacterized in that enrolled individuals are not provided withpersonal identification codes or personal identification cards that mustbe evaluated by the verification system to produce an output signalindicative of whether an enrolled individual is recognized.
 13. Theverification system of claim 9, wherein the software is adapted to scalethe newly acquired digital representation of a human facial image tosubstantially fit a predetermined size.